A. Feder Cooper

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 34

Top Topics

Metropolis Hastings

Nearest Neighbour Algorithm

Machine Learning

Stochastic Gradient

Top Venues

Publications

Daniel McDuff, Tim Korjakow, Scott Cambo, Jesse Josua Benjamin, Jenny Lee, Yacine Jernite, Carlos Muñoz Ferrandis, Aaron Gokaslan, Alek Tarkowski, Joseph Lindley, A. Feder Cooper, Danish Contractor
On the Standardization of Behavioral Use Clauses and Their Adoption for Responsible Licensing of AI. CoRR (2024)
A. Feder Cooper
Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale. CoRR (2024)
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M. Kleinberg, Siddhartha Sen, Baobao Zhang
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification. AAAI (2024)
A. Feder Cooper, James Grimmelmann
The Files are in the Computer: Copyright, Memorization, and Generative AI. CoRR (2024)
Nicholas Carlini, Daniel Paleka, Krishnamurthy (Dj) Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr
Stealing Part of a Production Language Model. CoRR (2024)
Katherine Lee, A. Feder Cooper, James Grimmelmann
Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain (The Short Version). CSLAW (2024)
Katherine Lee, A. Feder Cooper, James Grimmelmann
Talkin' 'Bout AI Generation: Copyright and the Generative-AI Supply Chain. CoRR (2023)
Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov
CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images. CoRR (2023)
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, Katherine Lee
Scalable Extraction of Training Data from (Production) Language Models. CoRR (2023)
A. Feder Cooper, Solon Barocas, Christopher De Sa, Siddhartha Sen
Variance, Self-Consistency, and Arbitrariness in Fair Classification. CoRR (2023)
A. Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide
Report of the 1st Workshop on Generative AI and Law. CoRR (2023)
A. Feder Cooper, Wentao Guo, Khiem Pham, Tiancheng Yuan, Charlie Ruan, Yucheng Lu, Christopher De Sa
CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training. NeurIPS (2023)
A. Feder Cooper, Jonathan Frankle, Christopher De Sa
Non-Determinism and the Lawlessness of ML Code. CoRR (2022)
A. Feder Cooper, Jonathan Frankle, Christopher De Sa
Non-Determinism and the Lawlessness of Machine Learning Code. CSLAW (2022)
A. Feder Cooper, Emanuel Moss, Benjamin Laufer, Helen Nissenbaum
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning. FAccT (2022)
A. Feder Cooper, Karen Levy
Fast or Accurate? Governing Conflicting Goals in Highly Autonomous Vehicles. CoRR (2022)
Benjamin Laufer, Sameer Jain, A. Feder Cooper, Jon M. Kleinberg, Hoda Heidari
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects. FAccT (2022)
A. Feder Cooper, Benjamin Laufer, Emanuel Moss, Helen Nissenbaum
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning. CoRR (2022)
A. Feder Cooper, Gili Vidan
Making the Unaccountable Internet: The Changing Meaning of Accounting in the Design of the Early Internet. CoRR (2022)
Benjamin Laufer, Sameer Jain, A. Feder Cooper, Jon M. Kleinberg, Hoda Heidari
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects. CoRR (2022)
A. Feder Cooper, Gili Vidan
Making the Unaccountable Internet: The Changing Meaning of Accounting in the Early ARPANET. FAccT (2022)
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Chris De Sa, Michael L. Littman
Model Selection's Disparate Impact in Real-World Deep Learning Applications. CoRR (2021)
A. Feder Cooper, Ellen Abrams, Na Na
Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research. AIES (2021)
A. Feder Cooper, Ellen Abrams
Emergent Unfairness: Normative Assumptions and Contradictions in Algorithmic Fairness-Accuracy Trade-Off Research. CoRR (2021)
A. Feder Cooper, Karen Levy, Christopher De Sa
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems. EAAMO (2021)
A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno
Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron. CoRR (2021)
A. Feder Cooper, Yucheng Lu, Christopher De Sa
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. CoRR (2021)
A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher De Sa
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. NeurIPS (2021)
A. Feder Cooper
Where Is the Normative Proof? Assumptions and Contradictions in ML Fairness Research. CoRR (2020)
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. CoRR (2020)
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. NeurIPS (2020)
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. CoRR (2020)
A. Feder Cooper, Karen Levy, Christopher De Sa
Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems. CoRR (2020)
Ruqi Zhang, A. Feder Cooper, Christopher De Sa
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. AISTATS (2020)